Centre de Recherche Cerveau et Cognition (CerCo), CNRS, Paul Sabatier University, Toulouse, France; Institute of Noetic Sciences (IONS), Petaluma, CA, United States.
Centre de Recherche Cerveau et Cognition (CerCo), CNRS, Paul Sabatier University, Toulouse, France; Institute of Noetic Sciences (IONS), Petaluma, CA, United States; Swartz Center of Computational Neuroscience (SCCN), University of California San Diego (UCSD), La Jolla, CA, United States.
Prog Brain Res. 2024;287:91-109. doi: 10.1016/bs.pbr.2024.04.004. Epub 2024 May 31.
Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.
可穿戴式脑电图 (EEG) 和心电图 (ECG) 设备可为评估真实环境中的幸福感 (WB) 提供一种非侵入性、用户友好且具有成本效益的方法。然而，在处理信号伪影（如环境噪声和运动）和识别稳健生物标志物方面仍存在挑战。我们评估了使用便携式硬件识别潜在 EEG 和心率变异性 (HRV) 与 WB 相关的可行性。我们使用连接到 4 通道可穿戴 EEG 耳机的腕部 ECG 电极，从真实环境中的 60 个人中同时采集超短 (2 分钟) EEG 和 ECG 数据。使用开源 EEGLAB BrainBeats 插件对这些数据进行处理、评估信号质量，并进行分析，以提取几个潜在的与 WB 相关的理论驱动指标。即 EEG 的个体阿尔法频率 (IAF)、额区和后区阿尔法不对称性以及信号熵。HRV 的 SDNN、低/高频 (LF/HF) 比、Poincaré SD1/SD2 比以及信号熵。我们通过实施两两相关方法、稳健的 Spearman 相关和多重比较校正，评估了这些特征与主要 WB 维度（享乐、幸福、整体、身体和社会）之间的潜在关联。只有八个文件显示信号质量差，被排除在分析之外。幸福（心理）WB 与 SDNN 和 LF/HF 比呈正相关。EEG 后区阿尔法不对称性与身体 WB（即睡眠和疼痛水平）呈正相关。与其他指标或 EEG 与 HRV 指标之间没有发现关系。这些生理指标可以使用可扩展、用户友好的工具快速、客观地评估真实环境中的幸福感。
